Establishment and application of reference equations for FEF50 and FEF75 in the Chinese population.

Background: Reference equations for forced expiratory flow at 50% and 75% of forced vital capacity (FVC) (FEF50 and FEF75) in the Chinese population are lacking. It is of great importance to establish equations covering most age groups and to study their applicability in clinical practice. Methods: Using the lambda-mu-sigma (LMS) method, reference equations for FEF50 and FEF75 were constructed based on pulmonary function data from healthy subjects collected from January 2007 to June 2010 at 24 centers throughout China. Differences between the established equations and extraneous equations were compared using standardized means (Z values) and percentage errors (PE). The proportion of small airway dysfunction (SAD) defined by the present equations was calculated. The Fisher precision probability test and the Mann-Whitney test were used to analyze the magnitude of changes in small and large airway indices after bronchodilator inhalation in patients with suspected asthma and chronic obstructive pulmonary disease (COPD). Results: Reference equations for FEF50 and FEF75 were established based on data from 7,115 healthy individuals (aged 4 to 80 years, 50.9% female, height between 95 and 190 cm). The present equations (all Z values were -0.0 and PE ranged from 2.0% to 4.2%) showed advantages over the European Community for Steel and Coal (ECSC) equations in 1993 (with Z values ranging from -0.7 to -0.2 and PE ranged from -23.4% to -4.5%). A total of 4,356 patients with suspected asthma (51.1% female; a mean age of 45.4 years) and 6,558 patients with suspected COPD (10.1% female; a mean age of 65.0 years) were included. The present equations defined 95.7% and 99.9% of SAD in these patients. After bronchodilator inhalation, greater mean improvement rates in small airway indices were observed both in patients with suspected asthma [mean ± standard deviation (SD) =48%±47%] and in patients with suspected COPD (mean ± SD =20%±30%) (P<0.05). Conclusions: The reference equations for FEF50 and FEF75 established in this study should be considered for use in China. Further studies are needed to validate their value in the diagnosis of some chronic respiratory diseases.

Hospitalization, case fatality, comorbidities, and isolated pathogens of adult inpatients with pneumonia from 2013 to 2022: a real-world study in Guangzhou, China.

BACKGROUND: In the context of increasing population aging, ongoing drug-resistant pathogens and the COVID-19 epidemic, the changes in the epidemiological and clinical characteristics of patients with pneumonia remain unclear. This study aimed to assess the trends in hospitalization, case fatality, comorbidities, and isolated pathogens of pneumonia-related adult inpatients in Guangzhou during the last decade. METHODS: We retrospectively enrolled hospitalized adults who had doctor-diagnosed pneumonia in the First Affiliated Hospital of Guangzhou Medical University from January 1, 2013 to December 31, 2022. A natural language processing system was applied to automatically extract the clinical data from electronic health records. We evaluated the proportion of pneumonia-related hospitalizations in total hospitalizations, pneumonia-related in-hospital case fatality, comorbidities, and species of isolated pathogens during the last decade. Binary logistic regression analysis was used to assess predictors for patients with prolonged length of stay (LOS). RESULTS: A total of 38,870 cases were finally included in this study, with 70% males, median age of 64 (53, 73) years and median LOS of 7.9 (5.1, 12.8) days. Although the number of pneumonia-related hospitalizations showed an upward trend, the proportion of pneumonia-related hospitalizations decreased from 199.6 per 1000 inpatients in 2013 to 123.4 per 1000 in 2021, and the case fatality decreased from 50.2 per 1000 in 2013 to 23.9 per 1000 in 2022 (all P < 0.05). The most common comorbidities were chronic obstructive pulmonary disease, lung malignancy, cardiovascular diseases and diabetes. The most common pathogens were Pseudomonas aeruginosa, Candida albicans, Acinetobacter baumannii, Stenotrophomonas maltophilia, Klebsiella pneumoniae, and Staphylococcus aureus. Glucocorticoid use during hospitalization (Odd Ratio [OR] = 1.86, 95% Confidence Interval (CI): 1.14-3.06), immunosuppressant use during hospitalization (OR = 1.99, 1.14-3.46), ICU admission (OR = 16.23, 95%CI: 11.25-23.83), receiving mechanical ventilation (OR = 3.58, 95%CI: 2.60-4.97), presence of other underlying diseases (OR = 1.54, 95%CI: 1.15-2.06), and elevated procalcitonin (OR = 1.61, 95%CI: 1.19-2.19) were identified as independent predictors for prolonged LOS. CONCLUSION: The proportion of pneumonia-related hospitalizations and the in-hospital case fatality showed downward trends during the last decade. Pneumonia inpatients were often complicated by chronic underlying diseases and isolated with gram-negative bacteria. ICU admission was a significant predictor for prolonged LOS in pneumonia inpatients.

Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT.

BACKGROUND: The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research. OBJECTIVES: To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT. METHODS: Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou. A deep learning framework was employed to develop two models: an undelineated supervised training (UST) model utilising original CT images, and a delineated supervised training (DST) model utilising CT images with manual lesion annotations provided by physicians. A subset of 20 cases was randomly selected from the entire dataset and reviewed by clinicians through a network questionnaire. The sensitivity, specificity and accuracy of the models and the clinicians were calculated. RESULTS: A total of 395 PC cases and 249 LA cases were included in the final analysis. The internal validation results for the UST model showed a sensitivity of 85.3%, specificity of 81.0%, accuracy of 83.6% and an area under the curve (AUC) of 0.93. Similarly, the DST model exhibited a sensitivity of 88.2%, specificity of 88.1%, accuracy of 88.2% and an AUC of 0.94. The external validation of the two models yielded AUC values of 0.74 and 0.77, respectively. The average sensitivity, specificity and accuracy of 102 clinicians were determined to be 63.1%, 53.7% and 59.3%, respectively. CONCLUSIONS: Both models outperformed the clinicians in distinguishing between PC and LA on chest CT, with the UST model exhibiting comparable performance to the DST model.

Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network.

Background: Artificial intelligence (AI) technology has been used for finding lesions via gastrointestinal endoscopy. However, there were few AI-associated studies that discuss bronchoscopy. Objectives: To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy. Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net. Methods: Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets. Results: In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear bronchoscopic images of 72 patients were used for validation. The mean accuracy of recognizing these 9 positions was 91% (carina: 98%, right main bronchus: 98%, right intermediate bronchus: 90%, right upper lobe bronchus: 91%, right middle lobe bronchus 92%, right lower lobe bronchus: 83%, left main bronchus: 89%, left upper bronchus: 91%, left lower bronchus: 76%). The area under the curves for these nine positions were >0.98. In addition, the accuracy of extracting the images via the video by the trained model was 94.7%. We also conducted a deep learning study to segment 10 segment bronchi in right lung, and 8 segment bronchi in Left lung. Because of the problem of radial depth, only segment bronchi distributions below right upper bronchus and right middle bronchus could be correctly recognized. The accuracy of recognizing was 84.33 ± 7.52% by doctors receiving interventional pulmonology education in our hospital over 6 months. Conclusion: Our study proved that AI technology can be used to distinguish the normal anatomical positions of the airway, and the model we trained could extract the corrected images via the video to help standardize data collection and control quality.

REALizing and improving management of stable COPD in China: results of a multicentre, prospective, observational study (REAL).

BACKGROUND: Chronic obstructive pulmonary disease (COPD) management in China is far from adequate; underdiagnosis and undertreatment are major barriers to optimal care and improved patient outcomes. OBJECTIVE: To generate reliable information on COPD management, outcomes, treatment patterns and adherence, and disease knowledge in China in a real-world setting. DESIGN: A 52-week multicentre, prospective, observational study. METHODS: Outpatients (⩾40 years old) diagnosed with COPD were enrolled from 50 secondary and tertiary hospitals across six geographical regions. Data were collected in routine clinical practice. RESULTS: Between June 2017 and January 2019, 5013 patients were enrolled and 4978 included in the analysis. Mean [standard deviation (SD)] age was 66.2 (8.9) years, 79.5% were male and 90% had moderate-to-very-severe airflow limitation. Annual rates of overall and severe exacerbation were 0.56 and 0.31, respectively. During 1 year, 1536 (30.8%) patients experienced ⩾1 exacerbation and 960 (19.3%) patients had ⩾1 exacerbation requiring hospitalization/emergency visit. Mean (SD) COPD assessment test score was 14.6 (7.6) at baseline and 10.6 (6.8) at follow-up; however, 42-55% of patients had persistent dyspnoea, chest tightness and wheezing at 1 year. The most prescribed treatments were inhaled corticosteroid (ICS)/long-acting ß2-agonist (LABA) (36.0%), ICS/LABA + long-acting muscarinic antagonist (LAMA) (17.7%) and LAMA monotherapy (15.3%). Among patients with high exacerbation risk (GOLD Groups C and D), 10.1% and 13.1%, respectively, did not receive any long-acting inhalers; only 53.8% and 63.6% of Group C and D patients with ⩾1 exacerbation during follow-up were prescribed ICS-containing therapy, respectively. Mean (SD) adherence for long-acting inhalers was 59.0% (34.3%). Mean (SD) score for the COPD questionnaire was 6.7 (2.4). CONCLUSION: These results indicate a high burden of severe exacerbations and symptoms in Chinese outpatients with COPD, and low adherence with treatment guidelines, highlighting the need for more effective management nationwide. REGISTRATION: The trial was registered on 20 March 2017 (ClinicalTrials.gov identifier: NCT03131362).

Exacerbation in patients with stable COPD in China: analysis of a prospective, 52-week, nationwide, observational cohort study (REAL).

BACKGROUND: Chronic obstructive pulmonary disease (COPD) management in China is inadequate and there is a need to improve care and outcomes for patients nationwide. OBJECTIVES: The REAL study was designed to generate reliable information on COPD management from a representative sample of Chinese patients with COPD. Here, we present study outcomes related to acute exacerbations. DESIGN: A 52-week, multicentre, prospective, observational study. METHODS: Outpatients (aged ⩾ 40 years) enrolled from 25 tertiary and 25 secondary hospitals across six geographic regions in China were followed for 12 months. Risk factors for COPD exacerbation and disease severity by exacerbation were assessed using multivariate Poisson and ordinal logistic regression models, respectively. RESULTS: Between June 2017 and January 2019, 5013 patients were enrolled, with 4978 included in the analysis. Mean (standard deviation) age was 66.2 (8.9) years. More patients presented with exacerbations in secondary versus tertiary hospitals (59.4% versus 40.2%) and in rural versus urban areas (53.2% versus 46.3%). Overall exacerbation rates differed across regions (range: 0.27-0.84). Patients from secondary versus tertiary hospitals had higher rates of overall exacerbation (0.66 versus 0.47), severe exacerbation (0.44 versus 0.18) and exacerbation that resulted in hospitalisation (0.41 versus 0.16). Across regions and hospital tiers, the rates of overall exacerbation and exacerbations that resulted in hospitalisation were highest in patients with very severe COPD (based on the severity of airflow limitation or GOLD 2017 combined assessment). Strong predictors of exacerbation included demographic and clinical characteristics, modified Medical Research Council scores, mucus purulence, exacerbation history and the use of maintenance mucolytic treatment. CONCLUSION: COPD exacerbation rates varied across regions and were higher in secondary compared with tertiary hospitals in China. Understanding the factors associated with COPD exacerbation may facilitate improved management of COPD exacerbations in China. REGISTRATION: The trial was registered on 20 March 2017 (ClinicalTrials.gov: NCT03131362; https://clinicaltrials.gov/ct2/show/NCT03131362). PLAIN LANGUAGE SUMMARY: Exacerbations in patients with chronic obstructive pulmonary disease in ChinaBackground: Chronic obstructive pulmonary disease (COPD) causes progressive and irreversible airflow limitation. As the disease progresses, patients often experience a flare up of symptoms referred to as an exacerbation. There is inadequate management of COPD in China and, therefore, there is a need to improve care and outcomes for patients across the country.Objective: This study aimed to generate reliable information on exacerbations among Chinese patients with COPD to help inform future management strategies.Study design and methods: Patients (aged ⩾ 40 years) were enrolled from 25 secondary and 25 tertiary hospitals across six regions of China. Physicians collected data over 1 year during routine outpatient visits.Results: There were more patients who experienced an exacerbation in secondary versus tertiary hospitals (59% versus 40%) and in rural versus urban areas (53% versus 46%). Patients in different geographic regions experienced varying frequencies of exacerbations over 1 year. Compared with patients from tertiary hospitals, patients from secondary hospitals experienced exacerbations (including exacerbations that were severe and those that resulted in hospitalisation) at a higher frequency over 1 year. Patients with very severe disease experienced exacerbations (including exacerbations that resulted in hospitalisation) at the highest frequency over 1 year, regardless of the patient's geographic region or hospital tier. Patients who had certain characteristics and symptoms, had exacerbation(s) over the previous year, or received medication that aids in the clearance of mucus were more likely to experience exacerbations.Conclusion: The frequency of exacerbations among Chinese patients with COPD varied between patients living in different geographic regions and between patients presenting to different hospital tiers. Understanding the factors related to the occurrence of an exacerbation may help physicians better manage the disease.

Treatment patterns in patients with stable COPD in China: analysis of a prospective, 52-week, nationwide, observational cohort study (REAL).

BACKGROUND: Underdiagnosis and undertreatment pose major barriers to optimal management of chronic obstructive pulmonary disease (COPD) in China. OBJECTIVE: The REAL trial was performed to generate reliable information on real-world COPD management, outcomes and risk factors among Chinese patients. Here, we present study outcomes related to COPD management. DESIGN: It is a 52-week, prospective, observational, multicentre study. METHODS: Outpatients (aged ⩾40 years) enrolled from 50 secondary and tertiary hospitals across six geographic regions of China were followed up for 12 months, with two onsite visits and by telephone every 3 months following baseline. RESULTS: Between June 2017 and January 2019, 5013 patients were enrolled and 4978 included in the analysis. Mean [standard deviation (SD)] age was 66.2 (8.9) years, the majority of patients were male (79.5%) and mean (SD) time since COPD diagnosis was 3.8 (6.2) years. The most common treatments at each study visit were inhaled corticosteroids/long-acting beta-agonists (ICSs/LABAs; 28.3-36.0%), long-acting muscarinic antagonists (LAMAs; 13.0-16.2%) and ICS/LABA + LAMA (17.5-18.7%), but up to 15.8% of patients at each visit received neither ICS nor long-acting bronchodilators. The use of ICS/LABA, LAMA and ICS/LABA + LAMA differed across regions and hospital tiers; up to fivefold, more patients received neither ICS nor long-acting bronchodilators in secondary (17.3-25.4%) versus tertiary hospitals (5.0-5.3%). Overall, rates of nonpharmacological management were low. Direct treatment costs increased with disease severity, but the proportion of direct treatment costs incurred due to maintenance treatment decreased with disease severity. CONCLUSION: ICS/LABA, LAMA and ICS/LABA + LAMA were the most frequently prescribed maintenance treatments for patients with stable COPD in China, although their use differed between region and hospital tier. There is a clear need for improved COPD management across China, particularly in secondary hospitals. REGISTRATION: The trial was registered on 20 March 2017 (ClinicalTrials.gov identifier: NCT03131362; https://clinicaltrials.gov/ct2/show/NCT03131362). PLAIN LANGUAGE SUMMARY: Treatment patterns in patients with COPD in ChinaBackground: Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease characterized by progressive and irreversible airflow limitation. In China, many patients with this disease do not receive a diagnosis or appropriate treatment.Objective: This study aimed to generate reliable information on the treatment patterns among patients with COPD in China to help inform future management strategies.Study design and methods: Patients (aged ⩾40 years) were enrolled from 50 hospitals across 6 regions of China and physicians collected data over the course of 1 year during routine outpatient visits.Results: The majority of patients were receiving long-acting inhaled treatments, which are recommended to prevent worsening of the disease. Up to 16% of patients in this study, however, did not receive any of these recommended treatments. The proportion of patients who received long-acting inhaled treatments differed across regions and hospital tiers; there were about five times more patients in secondary hospitals (about 25%) who did not receive these treatments compared with those in tertiary hospitals (about 5%). Guidelines recommend that pharmacological treatment should be complemented by nondrug treatment, but this was only received by a minority of patients in this study. Patients with higher disease severity incurred greater direct treatment costs compared with those with milder disease. Maintenance treatment costs made up a smaller proportion of overall direct costs for patients with higher disease severity (60-76%) compared with patients with milder disease (81-94%).Conclusion: Long-acting inhaled treatments were the most frequently prescribed maintenance treatments among patients with COPD in China, but their use differed between region and hospital tier. There is a clear need to improve disease management across China, especially in secondary hospitals.

Regularity and mechanism of fake crackle noise in an electronic stethoscope.

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Development and clinical application of an electronic health record quality control system for pulmonary aspergillosis based on guidelines and natural language processing technology.

Background: There are considerable differences in the diagnosis and treatment of pulmonary aspergillosis (PA) between specialized hospitals and primary hospitals or developed areas and underdeveloped areas in China. There is a lack of electronic systems that assist respiratory physicians in standardizing the diagnosis and treatment of PA. Methods: We extracted 26 quality control points from the latest guidelines related to PA, and developed a PA quality control system of electronic health record (EHR) based on natural language processing (NLP) techniques. We obtained PA patient records in the Department of Respiratory Medicine of the First Affiliated Hospital of Guangzhou Medical University to verify the effectiveness of the system comparing with manually evaluation of respiratory experts. Results: We successfully developed quality control system of PA; 699 PA medical records from EHR of the First Affiliated Hospital of Guangzhou Medical University between January 2015 and March 2020 were obtained and assessed by the system; 162 defects were found, which included 19 medical records with diagnostic defects, 76 medical records with examination defects, and 80 medical records with treatment defects; 200 medical records were sampled for validation, and found that the sensitivity and accuracy of quality control system for pulmonary aspergillosis (QCSA) were 0.99 and 0.96, F1 value was 0.85, and the recall rate was 0.77 compared with experts' evaluation. Conclusions: Our system successfully uses medical guidelines and NLP technology to detect defects in the diagnosis and treatment of PA, which helps to improve the management quality of PA patients.

Estimation of lung age via a spline method and its application in chronic respiratory diseases.

Lung age is a simplified concept that makes spirometry data easier to understand, but it is not widely used due to limitations in estimation methods. The aim of this study was to develop new equations to estimate lung age and to explore the application value of lung age in chronic respiratory diseases. Retrospective spirometric data of 18- to 80-year-old healthy subjects were used to develop the lung age estimation equations. Models were respectively built by multiple linear regression, piecewise linear regression, and the natural cubic spline method. Patients with chronic obstructive pulmonary disease (COPD) and asthma were subdivided into stages I-IV according to the severity of airflow limitation under the recommendation of the Global Initiative for Chronic Obstructive Lung Disease. Propensity score matching was performed to balance age, height and sex between healthy subjects and patients. The difference between lung age and chronological age (∆ lung age) of patients with COPD and asthma was analyzed. A total of 3409 healthy subjects, 280 patients with COPD and 285 patients with asthma data were included in the analysis. The lung age estimation equation with the best goodness of fit was built by the spline method and composed of FEV1, FEF50%, FEF75% and height as explanatory variables. ∆ lung age progressively increased with the degree of airflow limitation in patients with COPD or asthma. Lung age estimation equations were developed by a spline modeling method. Lung age may be used in the assessment of chronic respiratory patients.

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. METHODS: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. RESULTS: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). CONCLUSIONS: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry.

Severity distribution and treatment of chronic obstructive pulmonary disease in China: baseline results of an observational study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) receives low awareness and is undertreated in China. Understanding the burden and treatment of COPD across the nation is important for improving quality of care for this disease. This study aims to reveal the current situation of COPD severity distribution and management across China. METHODS: Baseline data from REALizing and Improving Management of Stable COPD in China, a multicentre, prospective, longitudinal, observational study, were analysed. Patients diagnosed with COPD as per Global Initiative for Chronic Obstructive Lung Disease 2016 (GOLD 2016) criteria were enrolled from 50 randomly selected hospitals (tertiary, 25; secondary, 25) across six geographical regions. Data were collected in routine clinical settings. RESULTS: Between 15 December 2017 and 6 August 2020, 5013 patients were enrolled and 4978 included in the full analysis set. Of these, 2459 (49.4%) reported ≥ 1 exacerbation within 12 months prior to study enrolment, with a mean annual rate of 0.9/patient, including 0.2/patient and 0.5/patient leading to emergency room visits and hospitalisation, respectively. Spirometry graded 458 (10.1%), 1886 (41.7%), 1558 (34.5%), and 616 (13.6%) were GOLD stage I-IV, and 536 (11.4%), 1034 (22.0%), 563 (12.0%), and 2566 (54.6%) were classified as GOLD 2016 Group A-D, respectively, without evident regional variations. Inhaled corticosteroids plus long-acting beta2-agonist (ICS/LABA, 1316 [26.4%]), ICS/LABA plus long-acting muscarinic antagonist (ICS/LABA + LAMA, 871 [17.5%]), and LAMA (754 [15.1%]) were prescribed at high rates across all groups and regions. Medications not recommended by GOLD were commonly prescribed (TCM, 578 [11.6%]; others, 951 [19.1%]), and 681 (13.7%) were not given ICS or long-acting bronchodilators. CONCLUSIONS: Disease burden among Chinese COPD outpatients is high. Improved guideline adherence for COPD treatment is needed. Trial registration ClinicalTrials.gov identifier, NCT03131362.

Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns.

INTRODUCTION: Spirometry, a pulmonary function test, is being increasingly applied across healthcare tiers, particularly in primary care settings. According to the guidelines set by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), identifying normal, obstructive, restrictive, and mixed ventilatory patterns requires spirometry and lung volume assessments. The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow-volume curves in identifying the ventilatory patterns. Further, the performance of the best model was compared with that of physicians working in lung function laboratories. METHODS: The gold standard for identifying ventilatory patterns was the rules of ATS/ERS guidelines. One physician chosen from each hospital evaluated the ventilatory patterns according to the international guidelines. Ten deep learning models (ResNet18, ResNet34, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet50_vc, SE_ResNet18_vd, VGG11, VGG13, and VGG16) were developed to identify patterns from the flow-volume curves. The patterns obtained by the best-performing model were cross-checked with those obtained by the physicians. RESULTS: A total of 18,909 subjects were used to develop the models. The ratio of the training, validation, and test sets of the models was 7:2:1. On the test set, the best-performing model VGG13 exhibited an accuracy of 95.6%. Ninety physicians independently interpreted 100 other cases. The average accuracy achieved by the physicians was 76.9 ± 18.4% (interquartile range: 70.5-88.5%) with a moderate agreement (κ = 0.46), physicians from primary care settings achieved a lower accuracy (56.2%), while the VGG13 model accurately identified the ventilatory pattern in 92.0% of the 100 cases (P < 0.0001). CONCLUSIONS: The VGG13 model identified ventilatory patterns with a high accuracy using the flow-volume curves without requiring any other parameter. The model can assist physicians, particularly those in primary care settings, in minimizing errors and variations in ventilatory patterns.

Dynamic changes of pulmonary diffusion capacity in survivors of non-critical COVID-19 during the first six months.

BACKGROUND: The dynamic trends of pulmonary function in coronavirus disease 2019 (COVID-19) survivors since discharge have been rarely described. We aimed to describe the changes of lung function and identify risk factors for impaired diffusion capacity. METHODS: Non-critical COVID-19 patients admitted to the Guangzhou Eighth People's Hospital, China, were enrolled from March to June 2020. Subjects were prospectively followed up with pulmonary function tests at discharge, three and six months after discharge. FINDINGS: Eighty-six patients completed diffusion capacity tests at three timepoints. The mean diffusion capacity for carbon monoxide (DLCO)% pred was 79.8% at discharge and significantly improved to 84.9% at Month-3. The transfer coefficient of the lung for carbon monoxide (KCO)% pred significantly increased from 91.7% at discharge to 95.7% at Month-3. Both of them showed no further improvement at Month-6. The change rates of DLCO% pred and KCO% pred were significantly higher in 0-3 months than in 3-6 months. The alveolar ventilation (VA) improved continuously during the follow-ups. At Month-6, impaired DLCO% pred was associated with being female (OR 5.2 [1.7-15.8]; p = 0.004) and peak total lesion score (TLS) of chest CT > 8.5 (OR 6.6 [1.7-26.5]; p = 0.007). DLCO% pred and KCO% pred were worse in females at discharge. And in patients with impaired diffusion capacity, females' DLCO% pred recovered slower than males. INTERPRETATION: The first three months is the critical recovery period for diffusion capacity. The impaired diffusion capacity was more severe and recovered slower in females than in males. Early pulmonary rehabilitation and individualized interventions for recovery are worthy of further investigations.

Clinical characteristics of patients with chronic obstructive pulmonary disease assessed using GOLD 2016 and GOLD 2018 classifications: a cross-sectional study in China.

BACKGROUND: In 2017, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) removed spirometry as a criterion for classifying GOLD risk groups (A-D, low-high risk). METHODS: In this cross-sectional observational study in China, we used the GOLD 2016 (spirometry included) and 2018 (spirometry eliminated) criteria for classifying GOLD risk groups to describe: the proportion of patients with chronic obstructive pulmonary disease (COPD) in each GOLD risk group; disease severity; demographics and comorbidities. Patients aged ≥40 years with a clinical COPD diagnosis for ≥1 year were included. During a single study visit, patients completed the COPD assessment test, modified Medical Research Council (mMRC) dyspnea scale assessment, and spirometry tests. Demographics, medical history, and treatment data were recorded. RESULTS: In total, 838 patients were included. Most patients were male (86.4%), ≥65 years old (58.6%), and current or former smokers (78.5%). By GOLD 2016, the highest proportion of patients were Group D (42.8%), followed by B (28.2%). By GOLD 2018, the highest proportion of patients were Group B (57.3%), followed by A (25.5%). A total of 296 patients (35.3%) were reclassified, either from Group C to Group A or from Group D to Group B. Overall, 36.2% of patients were receiving treatment concordant with GOLD 2016 recommendations; 34.1% were not receiving any inhaled medication. CONCLUSIONS: The distribution of COPD severity shifted from a high-risk category (by GOLD 2016) to a low-risk category (by GOLD 2018). The high proportion of patients not receiving maintenance medication reflects a high level of under-treatment of the disease.

Earlier diagnosis improves COVID-19 prognosis: a nationwide retrospective cohort analysis.

BACKGROUND: Risk of adverse outcomes in COVID-19 patients by stratifying by the time from symptom onset to confirmed diagnosis status is still uncertain. METHODS: We included 1,590 hospitalized COVID-19 patients confirmed by real-time RT-PCR assay or high-throughput sequencing of pharyngeal and nasal swab specimens from 575 hospitals across China between 11 December 2019 and 31 January 2020. Times from symptom onset to confirmed diagnosis, from symptom onset to first medical visit and from first medical visit to confirmed diagnosis were described and turned into binary variables by the maximally selected rank statistics method. Then, survival analysis, including a log-rank test, Cox regression, and conditional inference tree (CTREE) was conducted, regarding whether patients progressed to a severe disease level during the observational period (assessed as severe pneumonia according to the Chinese Expert Consensus on Clinical Practice for Emergency Severe Pneumonia, admission to an intensive care unit, administration of invasive ventilation, or death) as the prognosis outcome, the dependent variable. Independent factors included whether the time from symptom onset to confirmed diagnosis was longer than 5 days (the exposure) and other demographic and clinical factors as multivariate adjustments. The clinical characteristics of the patients with different times from symptom onset to confirmed diagnosis were also compared. RESULTS: The medians of the times from symptom onset to confirmed diagnosis, from symptom onset to first medical visit, and from first medical visit to confirmed diagnosis were 6, 3, and 2 days. After adjusting for age, sex, smoking status, and comorbidity status, age [hazard ratio (HR): 1.03; 95% CI: 1.01-1.04], comorbidity (HR: 1.84; 95% CI: 1.23-2.73), and a duration from symptom onset to confirmed diagnosis of >5 days (HR: 1.69; 95% CI: 1.10-2.60) were independent predictors of COVID-19 prognosis, which echoed the CTREE models, with significant nodes such as time from symptom onset to confirmed diagnosis, age, and comorbidities. Males, older patients with symptoms such as dry cough/productive cough/shortness of breath, and prior COPD were observed more often in the patients who procrastinated before initiating the first medical consultation. CONCLUSIONS: A longer time from symptom onset to confirmed diagnosis yielded a worse COVID-19 prognosis.

Longitudinal virological changes and underlying pathogenesis in hospitalized COVID-19 patients in Guangzhou, China.

Prolonged viral RNA shedding and recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in coronavirus disease 2019 (COVID-19) patients have been reported. However, the clinical outcome and pathogenesis remain unclear. In this study, we recruited 43 laboratory-confirmed COVID-19 patients. We found that prolonged viral RNA shedding or recurrence mainly occurred in severe/critical patients (P<0.05). The average viral shedding time in severe/critical patients was more than 50 days, and up to 100 days in some patients, after symptom onset. However, chest computed tomography gradually improved and complete absorption occurred when SARS-CoV-2 RT-PCR was still positive, but specific antibodies appeared. Furthermore, the viral shedding time significantly decreased when the A1,430G or C12,473T mutation occurred (P<0.01 and FDR<0.01) and increased when G227A occurred (P<0.05 and FDR<0.05). High IL1R1, IL1R2, and TNFRSF21 expression in the host positively correlated with viral shedding time (P<0.05 and false discovery rate <0.05). Prolonged viral RNA shedding often occurs but may not increase disease damage. Prolonged viral RNA shedding is associated with viral mutations and host factors.

Changes of quantitative CT-based airway wall dimensions in patients with COVID-19 during early recovery.

BACKGROUND: As the coronavirus disease 19 (COVID-19) pandemic evolves, the need for recognizing the structural pulmonary changes of the disease during early convalescence has emerged. Most studies focus on parenchymal destruction of the disease; but little is known about whether the disease affects the airway. This study was conducted to investigate the changes in airway dimensions and explore the associated factors during early convalescence in patients with COVID-19. METHODS: We retrospectively analyzed quantitative computed tomography (CT)-based airway measures of 69 patients with COVID-19 from 5 February to 17 March 2020, and 32 non-COVID-19 participants from 1 January 2018 to 31 December 2019 from Guangzhou, China. The well-established measures of wall area fraction and the square root of the wall area of a hypothetical bronchus with an inner perimeter of 10 mm, were used to describe airway wall dimensions. We described the characteristics of the dimensions and inner area of airways in 66 patients with COVID-19 at the initial and convalescent stages of the disease, and compared them with the non-COVID-19 group. Linear regression models were constructed to investigate the association of airway dimensions with duration of hospitalization or disease severity after recovery. Partial correlation coefficients were calculated to investigate whether inflammatory markers were related to airway dimensions. RESULTS: Among 66 patients with COVID-19, airway dimensions were greater during disease initiation than early convalescence, which was significantly greater than in non-COVID-19 participants. No significant difference was found between the patients with COVID-19 at the initial stage and the non-COVID-19 controls regarding the first to eighth generations of the inner area. In adjusted regression models, duration of hospitalization was negatively associated with wall area fraction of the first to the sixth generation of airways. No significant associations exist between airway dimensions and disease severity, or airway dimensions with inflammatory markers. CONCLUSIONS: Airway dimensions in patients with COVID-19 during disease initiation are greater than those in non-COVID-19 participants. Such structural airway changes continue to remain significantly greater during early convalescence. No evidence shows that disease severity or inflammatory markers are associated with airway dimensions, implying that the primary lesion attacked by COVID-19 might not be the airways.

Trends of pulmonary fungal infections from 2013 to 2019: an AI-based real-world observational study in Guangzhou, China.

Recently, the prevalence trend of pulmonary fungal infection (PFI) has rapidly increased. Changes in the risk factors for, distributions of underlying diseases associated with and clinical characteristics of some individual PFIs have been reported in the past decade. However, data regarding PFIs remain uncertain. This study reports the epidemiological characteristics and trends of PFIs over time in recent years. We applied an automated natural language processing (NLP) system to extract clinically relevant information from the electronic health records (EHRs) of PFI patients at the First Affiliated Hospital of Guangzhou Medical University. Then, a trend analysis was performed. From January 1, 2013, to December 31, 2019, 40,504 inpatients and 219,414 outpatients with respiratory diseases were screened, in which 1368 inpatients and 1313 outpatients with PFI were identified. These patients were from throughout the country, but most patients were from southern China. Upward trends in PFIs were observed in both hospitalized patients and outpatients (P<0.05). The stratification by age showed that the incidence of hospitalized patients aged 14-30 years exhibited the most obvious upward trend, increasing from 9.5 per 1000 patients in 2013 to 88.3 per 1000 patients in 2019. Aspergillosis (56.69%) was the most common PFI, but notably, the incidence rates of Talaromyces marneffei, which used to be considered uncommon, exhibited the most rapid increases. In younger PFI patients, the incidence and trend of PFIs have increased. Infection by previously uncommon pathogens has also gradually increased. Increased attention should be paid to young PFI patients and uncommon PFI pathogen infections.